AI Red Teaming: Securing LLMs and Agentic AI Systems

As Large Language Models (LLMs) and autonomous AI agents become deeply integrated into software systems, securing them against novel vulnerabilities is a critical priority. This course provides a comprehensive introduction to AI red teaming, equipping you with the mindset and techniques needed to proactively find and patch security flaws in AI applications. You will transition from understanding basic security boundaries to identifying complex system-level exploits. Under the guidance of structured written explanations, you will learn to: Understand core AI security concepts, threat modeling, and the OWASP Top 10 vulnerabilities for LLMs; Analyze prompt injection, jailbreaking, and training data poisoning techniques; Evaluate risks unique to agentic AI, including tool-use exploitation and logic loop hijacking; Implement robust guardrails, input sanitization, and defensive alignment strategies to secure applications. This course begins with foundational definitions of AI architectures and security parameters, before guiding you through step-by-step written threat scenarios and mitigation strategies. Designed for developers, security enthusiasts, and systems engineers new to AI security, this program requires no prior red teaming experience. Start reading today to build safer, more resilient AI integrations.